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Information
| Code | EMG28720 |
| Name | Data Mining |
| Term | 2022-2023 Academic Year |
| Semester | . Semester |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | Türkçe |
| Level | Yüksek Lisans Dersi |
| Type | Normal |
| Mode of study | Uzaktan Öğretim |
| Catalog Information Coordinator |
Course Goal / Objective
Teaching the application processes of what can be done within the scope of machine learning and data mining with WEKA and R languages. Teaching what can be done on big data.
Course Content
Machine learning, data mining, artificial intelligence concepts and application with WEKA and R languages. Analyzes that can be applied on big data.
Course Precondition
None
Resources
Data Mining. Parteek Bhatia
Notes
There is no additional text book in this course.
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Explain the concepts of machine learning and artificial intelligence. |
| LO02 | Lists the operations that can be done within the scope of data mining. |
| LO03 | Explains the classification methods required to construct a decision tree. |
| LO04 | Recognizes the WEKA program, which is an open source software, and uses it for data mining. |
| LO05 | It recognizes the R language, which is an open source software, and uses codes for data mining. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Defining the basic functions of the business and explaining their relations with each other from the point of view of technology. | |
| PLO02 | Bilgi - Kuramsal, Olgusal | Defining the basic numerical and statistical methods that can be used in solving problems that may be encountered in businesses. | 1 |
| PLO03 | Bilgi - Kuramsal, Olgusal | To apply numerical and statistical methods and models used in problem solving in businesses. | 5 |
| PLO04 | Bilgi - Kuramsal, Olgusal | Interpreting the models created for the problems by solving them with software. | 5 |
| PLO05 | Beceriler - Bilişsel, Uygulamalı | To be able to define business problems arising from technological and global changes. | 2 |
| PLO06 | Beceriler - Bilişsel, Uygulamalı | To be able to solve basic business problems with analytical thinking ability. | 4 |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | To be able to reach the most appropriate result by using numerical and statistical analysis programs in solving the problems arising from the production process and supply chain of the enterprise. | 3 |
| PLO08 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Working effectively as a team member, taking responsibility individually and/or within the team. | |
| PLO09 | Yetkinlikler - Öğrenme Yetkinliği | Self-development by being aware of change in business life and following technological developments. | 4 |
| PLO10 | Yetkinlikler - Öğrenme Yetkinliği | Synthesizing the information obtained by using different sources within the framework of academic rules. | 1 |
| PLO11 | Yetkinlikler - Öğrenme Yetkinliği | Applying technological changes and developments to their own field. | 5 |
| PLO12 | Yetkinlikler - Öğrenme Yetkinliği | To interpret the possible consequences of changes in environmental conditions and technology on the business and its functions. | 1 |
| PLO13 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Effectively presenting the information and comments obtained by using different sources within the framework of academic rules, verbally and in writing. | 1 |
| PLO14 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Effectively using new communication channels that have emerged with technological development in written and oral presentations. | 1 |
| PLO15 | Yetkinlikler - Alana Özgü Yetkinlik | To act in accordance with ethical and legal issues encountered in business science and different professional fields. | |
| PLO16 | Yetkinlikler - Alana Özgü Yetkinlik | Identifying the problems that arise in the supply chain and suggesting technological solutions. | 3 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Machine learning | Reading related parts | Öğretim Yöntemleri: Anlatım, Tartışma |
| 2 | Artificial intelligence | Reading related parts | Öğretim Yöntemleri: Anlatım, Tartışma |
| 3 | Introduction to data mining | Reading related parts | Öğretim Yöntemleri: Anlatım, Tartışma |
| 4 | Getting started with Weka | Reading related parts | Öğretim Yöntemleri: Anlatım, Gösterip Yaptırma |
| 5 | Getting started with R | Reading related parts | Öğretim Yöntemleri: Anlatım, Gösterip Yaptırma |
| 6 | Data preprocessing | Reading related parts | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 7 | Classification | Reading related parts | Öğretim Yöntemleri: Anlatım |
| 8 | Midterm Exam | Studying for exam | Ölçme Yöntemleri: Ödev |
| 9 | Classification applications with Weka | Reading related parts | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 10 | Classification applications with R language | Reading related parts | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 11 | Cluster analysis | Reading related parts | Öğretim Yöntemleri: Anlatım |
| 12 | Clustering applications with Weka and R | Reading related parts | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 13 | Association rule | Reading related parts | Öğretim Yöntemleri: Anlatım, Tartışma |
| 14 | Web Mining and Search Engines | Reading related parts | Öğretim Yöntemleri: Anlatım, Gösteri |
| 15 | Data warehouse and big data | Reading related parts | Öğretim Yöntemleri: Anlatım |
| 16 | Final Exam 1 | Preparation for Exam | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Final Exam 2 | Sınava Hazırlık | Ölçme Yöntemleri: Yazılı Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 3 | 42 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 5 | 70 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 15 | 15 |
| Final Exam | 1 | 30 | 30 |
| Total Workload (Hour) | 157 | ||
| Total Workload / 25 (h) | 6,28 | ||
| ECTS | 6 ECTS | ||